Botswana Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: % Cumulative: Male data was reported at 10.049 % in 2023. This records an increase from the previous number of 9.254 % for 2022. Botswana Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: % Cumulative: Male data is updated yearly, averaging 9.254 % from Dec 2019 (Median) to 2023, with 5 observations. The data reached an all-time high of 10.049 % in 2023 and a record low of 8.833 % in 2019. Botswana Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: % Cumulative: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Botswana – Table BW.World Bank.WDI: Social: Education Statistics. The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;;

Graph and download economic data for Ratio of Female to Male Tertiary School Enrollment for Botswana (SEENRTERTFMZSBWA) from 1972 to 2023 about Botswana, enrolled, ratio, females, tertiary schooling, males, and education.

Botswana Educational Attainment: At Least Competed Short-Cycle Tertiary: Population 25+ Years: % Cumulative: Female data was reported at 28.052 % in 2023. This records an increase from the previous number of 28.046 % for 2022. Botswana Educational Attainment: At Least Competed Short-Cycle Tertiary: Population 25+ Years: % Cumulative: Female data is updated yearly, averaging 27.473 % from Dec 2019 (Median) to 2023, with 5 observations. The data reached an all-time high of 28.052 % in 2023 and a record low of 26.827 % in 2020. Botswana Educational Attainment: At Least Competed Short-Cycle Tertiary: Population 25+ Years: % Cumulative: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Botswana – Table BW.World Bank.WDI: Social: Education Statistics. The percentage of population ages 25 and over that attained or completed short-cycle tertiary education.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;;

Percentage of enrolment in tertiary education in private institutions (%) in Botswana was reported at 35.54 % in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. Botswana - Percentage of enrolment in tertiary education in private institutions - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Graph and download economic data for Ratio of Female to Male Secondary School Enrollment for Botswana (SEENRSECOFMZSBWA) from 1970 to 2021 about Botswana, enrolled, secondary schooling, secondary, ratio, females, males, and education.

Time series data for the statistic Government expenditure on education not specified by level, constant US$ (millions) and country Botswana. Indicator Definition:Total general (local, regional and central) government expenditure on education (current, capital, and transfers) not specified by level in millions US$ in constant value (taking into account inflation). It includes expenditure funded by transfers from international sources to government. Total government expenditure for a given level of education (e.g. primary, secondary, or all levels combined) in national currency is converted to US$, and where it is expressed in constant value, uses a GDP deflator to account for inflation. The constant prices base year is normally three years before the year of the data release. For example, in the July 2017 data release, constant US$ values are expressed in 2014 prices. Limitations: In some instances data on total government expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/

Historical dataset showing Botswana literacy rate by year from 1991 to 2013.

Botswana Educational Attainment: Doctoral or Equivalent: Population 25+ Years: % Cumulative: Male data was reported at 0.444 % in 2023. This records a decrease from the previous number of 0.572 % for 2022. Botswana Educational Attainment: Doctoral or Equivalent: Population 25+ Years: % Cumulative: Male data is updated yearly, averaging 0.367 % from Dec 2019 (Median) to 2023, with 5 observations. The data reached an all-time high of 0.572 % in 2022 and a record low of 0.000 % in 2021. Botswana Educational Attainment: Doctoral or Equivalent: Population 25+ Years: % Cumulative: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Botswana – Table BW.World Bank.WDI: Social: Education Statistics. The percentage of population ages 25 and over that attained or completed Doctoral or equivalent.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;;

Time series data for the statistic Government expenditure on tertiary education as % of GDP (%) and country Botswana. Indicator Definition:Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/The indicator "Government expenditure on tertiary education as % of GDP (%)" stands at 4.00 as of 12/31/2009, the highest value at least since 12/31/1972, the period currently displayed. Regarding the Five-Year-Change of the series, the current value constitutes an increase of 14.01 percent compared to the value five years prior.The 5 year change in percent is 14.01.The Serie's long term average value is 1.08. It's latest available value, on 12/31/2009, is 269.92 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1971, to it's latest available value, on 12/31/2009, is +1,088.35%.The Serie's change in percent from it's maximum value, on 12/31/2009, to it's latest available value, on 12/31/2009, is 0.0%.

School enrollment, tertiary (% gross) in Botswana was reported at 21.79 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Botswana - School enrollment, tertiary (% gross) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

The urban indicators data available here are analyzed, compiled and published by UN-Habitat’s Global Urban Observatory which supports governments, local authorities and civil society organizations to develop urban indicators, data and statistics. Urban statistics are collected through household surveys and censuses conducted by national statistics authorities. Global Urban Observatory team analyses and compiles urban indicators statistics from surveys and censuses. Additionally, Local urban observatories collect, compile and analyze urban data for national policy development. Population statistics are produced by the United Nations Department of Economic and Social Affairs, World Urbanization Prospects.

Botswana BW: Secondary Education: Pupils data was reported at 175,513.000 Person in 2021. This records an increase from the previous number of 168,220.000 Person for 2008. Botswana BW: Secondary Education: Pupils data is updated yearly, averaging 53,849.000 Person from Dec 1970 (Median) to 2021, with 37 observations. The data reached an all-time high of 178,314.000 Person in 2007 and a record low of 5,197.000 Person in 1970. Botswana BW: Secondary Education: Pupils data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Botswana – Table BW.World Bank.WDI: Social: Education Statistics. Secondary education pupils is the total number of pupils enrolled at secondary level in public and private schools.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Sum;

Abstract

In 1991 the International Institute for Educational Planning (IIEP) and a number of Ministries of Education in Southern and Eastern Africa began to work together in order to address training and research needs in Education. The focus for this work was on establishing long-term strategies for building the capacity of educational planners to monitor and evaluate the quality of their basic education systems. The first two educational policy research projects undertaken by SACMEQ (widely known as "SACMEQ I" and "SACMEQ II") were designed to provide detailed information that could be used to guide planning decisions aimed at improving the quality of education in primary school systems.

During 1995-1998 seven Ministries of Education participated in the SACMEQ I Project. The SACMEQ II Project commenced in 1998 and the surveys of schools, involving 14 Ministries of Education, took place between 2000 and 2004. The survey was undertaken in schools in Botswana, Kenya, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Uganda, Zambia and Zanzibar.

Moving from the SACMEQ I Project (covering around 1100 schools and 20,000 pupils) to the SACMEQ II Project (covering around 2500 schools and 45,000 pupils) resulted in a major increase in the scale and complexity of SACMEQ's research and training programmes.

SACMEQ's mission is to: a) Expand opportunities for educational planners to gain the technical skills required to monitor and evaluate the quality of their education systems; and b) Generate information that can be used by decision-makers to plan and improve the quality of education.

Geographic coverage

National coverage

Analysis unit

• Pupils
• Teachers
• Schools

Universe

The target population for SACMEQ's Initial Project was defined as "all pupils at the Grade 6 level in 1995 who were attending registered government or non-government schools". Grade 6 was chosen because it was the grade level where the basics of reading literacy were expected to have been acquired.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling The "best" sample design for a particular project is one that provides levels of sampling accuracy that are acceptable in terms of the main aims of the project, while simultaneously limiting cost, logistic, and procedural demands to manageable levels. The major constraints that were established prior to the preparation of the sample designs for the SACMEQ II Project have been listed as follows.

Target Population: The target population definitions should focus on Grade 6 pupils attending registered mainstream government or non-government schools. In addition, the defined target population should be constructed by excluding no more than 5 percent of pupils from the desired target population.

Bias Control: The sampling should conform to the accepted rules of scientific probability sampling. That is, the members of the defined target population should have a known and non-zero probability of selection into the sample so that any potential for bias in sample estimates due to variations from "epsem sampling" (equal probability of selection method) could be addressed through the use of appropriate sampling weights.

Sampling Errors: The sample estimates for the main criterion variables should conform to the sampling accuracy requirements that the standard error of sampling for the pupil tests should be of a magnitude that is equal to, or smaller than, what would be achieved by employing a simple random sample of 400 pupils.

The Specification of the Target Population For Botswana, the desired target population was all pupils enrolled in Grade 6 in the '9th month of the school year (i.e., in September 2000). The net enrolment ratio in Botswana in 2001 was 87.2. However, in Botswana it was decided to exclude certain pupils. These were pupils in schools having fewer than 15 Grade 6 pupils in them, pupils in 'inaccessible schools, and pupils in special schools. In all 1512 pupils from 117 schools were excluded but this only amounted to 3.5 percent of all pupils. In Botswana there were 723 schools having 42804 pupils. After excluding the 3.5 percent of pupils the defined population from which a sample had to be drawn consisted of 41292 pupils from 606 schools.

The number of school required in the sample is in part a function of the intra-class correlation (rho) which is an indicator of the proportion of variation (in achievement in this case) among schools of total variation.

The rho value for Botswana was thought to be about 0.30. That is 30 percent of the variation was among schools and 70 percent within schools. Therefore, in the case of Botswana a rho of 0.30 was used. In the end a sample of 166 schools was drawn.

Note: Details of sampling design procedures are presented in the "Botswana Working Report".

Mode of data collection

Face-to-face [f2f]

Research instrument

The data collection for SACMEQ’s Initial Project took place in October 1995 and involved the administration of questionnaires to pupils, teachers, and school heads. The pupil questionnaire contained questions about the pupils’ home backgrounds and their school life; the teacher questionnaire asked about classrooms, teaching practices, working conditions, and teacher housing; and the school head questionnaire collected information about teachers, enrolments, buildings, facilities, and management. A reading literacy test was also given to the pupils. The test was based on items that were selected after a trial-testing programme had been completed.

Cleaning operations

A team of 6 temporary staff were appointed and trained in the use of WINDEM, a special data entry package to be used in SACMEQ. The numbers of keystrokes required to enter one copy of each data collection instrument were as follows: pupil questionnaire: 150; pupil reading test: 85; pupil mathematics test: 65; teacher questionnaire: 587; teacher reading test: 51; teacher mathematics test: 43; school head questionnaire: 319; school form: 58; and pupil name form: 51

In the case of Botswana the total number of keystrokes was as follows: pupil questionnaire: 762,600; pupil reading test: 429,080; pupil mathematics test: 328,250; teacher questionnaire: 358,657; teacher reading test: 15,504; teacher mathematics test: 14,061; school head questionnaire: 86,130; school form: 39,150; and pupil name form: 259,284. That is, a total of 2,292,716 keystrokes were required to enter all of the data for Botswana.

An experienced keyboard operator can work at a rate of 25 keystrokes per minute (working from multi-paged questionnaires and stopping occasionally to clarify individual questionnaire entries with the supervisor). Assuming that this kind of work rate could be sustained for, say, around a maximum of six hours per day, then the whole data entry operation for Botswana was estimated to amount to around 255 person days of data entry work. This implied an estimated five weeks of work for the 10-person data entry team that operated in Botswana. However, the work was completed in 3 months.

At the end of this procedure the data files were sent by email to the unit 'Monitoring Educational Quality' at the IIEP in Paris. Many consistency checks were made for many variables as well as for the identification codes used. The IIEP team had many queries. The first data files were sent to Paris in February of 2001 and after fifteen to-ings and fro-ings the files were finally declared to be clean on 5 December of 2001.

Response rate

Response rates for pupils and schools respectively were 91.8 percent and 100 percent. The reason for the shortfall in learner numbers was absenteeism by some learners in some of the schools on the day of data collection. However, sampling weights were used to correct for disproportionality among strata in the calculation of all statistics.

Sampling error estimates

The sample designs employed in the SACMEQ Projects departed markedly from the usual "textbook model" of simple random sampling. This departure demanded that special steps be taken in order to calculate "sampling errors" (that is, measures of the stability of sample estimates of population characteristics).

Abstract

This report presents results for data collected in the Q1 (January to March) of 2024. It presents information on employment, unemployment, formal sector wages, youth labour force and other labour force indicators.

It is important to note that results of quarterly surveys are almost always subjected to seasonal variations. This survey is no exception and thus the results that will be obtained over the four quarters of any year will have a seasonal effect. Assessment of the results for long term trends would be better done through analysis of year-on-year quarterly changes.

Statistics Botswana will continue to engage stakeholders on the content of this publication to ensure that it responds to their needs and also invite them to participate in the analysis of modules which shall be covered on a rotational basis. I hope you find this publication informative and useful for monitoring and decision making

Geographic coverage

The coverage would be nation-wide using census districts (administrative district and sub-districts) that are usually used by Statistics Botswana in most surveys and censuses.

Analysis unit

Household

Sampling procedure

The Sampling Frame and sample selection are based on the last Population and Housing Census which was undertaken in 2022. In 2022 PHC, there were 6 509 Primary Sampling Units (PSUs) and 750 611 households constituting the sampling frame with a population of 2,359, 609 persons. Only private dwellings would be within the scope of the survey. Institutional dwellings (prisons, hospitals, army barracks, hotels, etc.), and EAs with completely industrial area buildings would not be within the scope of the survey. The coverage would be nation-wide using census districts (administrative district and sub-districts) that are usually used by Statistics Botswana in most surveys and censuses.

The census result gives information on population, number of household at Locality, Enumeration Area (EA), village and district/town levels. Also given for each EA is information on ecological zones in rural areas. The Sampling frame was defined and constituted by all Enumeration Areas (EAs) found in three geographical strata, otherwise known as domains and these are (i) Cities & Towns (ii) Urban Villages, and (iii) Rural areas as defined by the 2022 PHC.

Based on the final calculation of QMTS 2022, a sample of 312 PSUs, translating to 3,747 households was selected with Probability Proportional to Size (PPS) method; where Measure of size (MOS) is the number of households as enumerated from the 2022 Population & Housing Census.

Mode of data collection

Computer Assisted Personal Interview

Abstract

The SACMEQ III Project commenced in 2006 and was completed during 2011. The SACMEQ III data collection was implemented in fifteen SACMEQ Ministries of Education (Botswana, Kenya, Lesotho, Mauritius, Malawi, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania (Mainland), Tanzania (Zanzibar), Uganda, Zambia, and Zimbabwe). The SACMEQ III Project followed the general research direction of the first two SACMEQ Projects by focusing on an examination of the conditions of schooling in relation to achievement levels of learners and their teachers in reading, and mathematics. The focus was expanded to cover the learners’ levels of basic knowledge about HIV and AIDS. The SACMEQ III Project involved data collections from around 61,000 learners, 8,000 teachers, and 2,800 school principals.

Geographic coverage

National Coverage

Analysis unit

• Pupils
• Schools
• Teachers

Universe

The desired target population for the SACMEQ III study was defined as "All pupils at Grade 6 level in 2007 (at the first week of the eighth month of the school year) who were attending registered mainstream primary schools". This definition used a grade-based description (and not an age-based description) of pupils because an age-based description would have required the collection of data across many grade levels due to the high incidences of "late starters" and grade repetition in SACMEQ school systems.

Kind of data

Sample survey data [ssd]

Sampling procedure

The desired target population definition for the SACMEQ III Project was exactly the same (except for the year) as was employed for the SACMEQ I and II Projects. This consistency was maintained in order to make valid cross-national and cross-time estimates of "change" in the conditions of schooling and the quality of education.

The SACMEQ III data were selected using a stratified two-stage cluster sample design based on the technique of a lottery method of sampling proportional to size, with the assistance of SAMDEM software (Sylla et al., 2003). At the first stage, schools were selected in each region (province) in proportion to the number of pupils in that region in the defined target population. The main reason for choosing Region as the explicit stratification variable was that the SACMEQ Ministries of Education wanted to have education administration regions as "domains" for the study. That is, the Ministries wanted to have a reasonably accurate sample estimates of population characteristics for each region. At the second stage, a simple random sample of 25 pupils was taken within each selected school (in the Seychelles, all Grade 6 pupils in all 25 schools in the island country were tested).

In educational survey research the primary sampling units that are most often employed (schools) are rarely equal in size. This variation causes difficulties with respect to the control of the total sample size when schools are selected with equal probability at the first stage of a multi-stage sample design. One method of obtaining greater control over the total sample size is to stratify the schools accorging to size and then select samples of schools within each stratum. A more widely applied alternative is to employ probability proportional to size (PPS) sampling of schools within strata followed by the selection of a simple random sample size and results in epsem sampling of pupils within strata. The lottery method of PPS selection was implemented for the SACMEQ Projects with the assistance of the SAMDEM software (Sylla et al, 2003).

In order to avoid selection bias, precautions were taken to ensure that school heads and teachers did not have any influence over the sampling procedures within schools. This is because school heads and teachers might have felt they had a vested interest in selecting particular kinds of pupils, and this could have resulted in major distortions of sample estimates (Brickell, 1974).

The planned Mauritian sample was 152 schools and 3950 pupils. There were six schools with two sample groups due to large number of Standard 6 pupils. The achieved sample comprised 152 schools and 3524 pupils. Pupils who were sampled in the sampled schools but were not available on the day of data collection were not replaced.

Stratification used to define population In all, seven regions were defined as strata for the target population as follows: Region 1, Port Louis & North (1PL): government schools in Education Zone 1. Region 2, Beau Bassin & East (2BB): government schools in Education Zone 2. Region 3, Curepipe & South (3CU): government schools in Education Zone 3. Region 4, Vacoas & West (4VA): government schools in Education Zone 4. Region 5, Rodrigues (5RO): all schools in the Island of Rodrigues. Region 6, Black River (6BR): all schools in Black River District. Region 7, Private (7PR): private schools from different education zones.

Sampling deviation

Excluded Target Population One of the rules followed by SACMEQ for ensuring valid data in large-scale studies is that no more than 5 percent of the learners in the desired target population may be excluded from the defined target population. Like in SACMEQ II, special schools which provide education to learners with severe educational needs were excluded from the SACMEQ III sample. “Small” mainstream schools which had less than 15 learners enrolled in Grade 6 in 2007 were also allocated to the excluded population to reduce data collection costs – without the risk of leading to major distortions in the study population.

Mode of data collection

Face-to-face [f2f]

Research instrument

• Apart from pupil achievement scores, SACMEQ studies are renowned for collecting a wide range of information about pupils, teachers, classrooms, school heads, schools, and school communities. For the SACMEQ III study, four main questionnaires (pupil, teacher, school head, and school information) were used.
• It is important to note that SACMEQ questionnaires were subjected to careful thought, thorough examination, and stringent refinement before they were administered. For example, for the SACMEQ III study, the questionnaires were developed by a committee of experts consisting of members drawn from all SACMEQ countries, SCC staff, IIEP staff, and private consultants, following (a) Field experiences gained from the SACMEQ II study, (b) Recommendations arising from analyses of SACMEQ II data, and (c) policy questions raised by SACMEQ country ministries of education. These questionnaires were refined by the SACMEQ scientific committee, then piloted in each SACMEQ country and refined further before they were administered.
• One important innovation in the development of questionnaires for the SACMEQ III study was introduction of a “Homework form” for pupils to take home. This consisted of questions to which the pupil might not know the answers (for example, parental education, estimates of travel distance to school, home possessions, whether or not their biological parents were alive) that parents, family members, or guardians could help in filling in. This considerably reduced the number of missing values in the SACMEQ III study compared with previous SACMEQ studies.
• Materials were translated into Kiswahili (Tanzania Mainland and Zanzibar) and Portuguese (Mozambique).

Cleaning operations

Data Entry was done using WinDEM (Windows Data Entry Manager) Software. Preliminary data cleaning involved checks on data to ensure it was clean before it was sent to the SACMEQ Coordinating Centre (SCC) for further checks an analysis and calculation of sampling weights. (See p12 of the NRC Manual - provided as external resources - for more detail on the process.)

Data Checking and Data Entry The Mauritian NRC received the completed materials from the data collectors and kept these safely while they were being checked, entered into computers, and then “cleaned” to remove errors prior to data analysis. Data- checking involved the “hand editing” of data collection instruments by a team of trained staff. The staff checked that: (i) All expected questionnaires, tests, and forms had been received, (ii) The identification numbers on all instruments were complete and accurate, and (iii) Certain logical linkages between questions made sense (for example, they had to verify if the two questions to School Heads concerning “Do you have a school library?” and “How many books do you have in your school library?” were answered consistently). Trained data capturers from Central Information Systems Division (CISD), supervised by the NRC, entered data into computers using the WINDEM software that was supplied by the SACMEQ Coordinating Centre. Data were “double entered” in order to monitor accuracy.

Data Cleaning During December 2007, the SACMEQ Coordinating Centre organized a training programme for all NRCs. The teams were led step-by-step through the required data cleaning procedures that they were to follow in their respective countries. At individual country level, NRTs followed a “cyclical” process whereby data files were cleaned by the NRT and then emailed to the Coordinating Centre for checking and then emailed back to the NRC for further cleaning. The entire data cleaning process in Mauritius lasted from January 2008 to August 2009. To clean the data, using the WINDEM software, the NRCs followed specific directions to (i) Identify major errors in the sequence of identification numbers, (ii) Cross-check identification numbers across files (for example, to ensure that all pupils were linked with their own Reading and Mathematics teachers), (iii) Ensure that all

Botswana BW: School Enrollment: Tertiary: % Gross data was reported at 21.791 % in 2023. This records a decrease from the previous number of 22.377 % for 2022. Botswana BW: School Enrollment: Tertiary: % Gross data is updated yearly, averaging 5.870 % from Dec 1970 (Median) to 2023, with 48 observations. The data reached an all-time high of 27.167 % in 2015 and a record low of 0.000 % in 1971. Botswana BW: School Enrollment: Tertiary: % Gross data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Botswana – Table BW.World Bank.WDI: Social: Education Statistics. Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

Botswana Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: % Cumulative: Female data was reported at 9.298 % in 2023. This records a decrease from the previous number of 9.741 % for 2022. Botswana Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: % Cumulative: Female data is updated yearly, averaging 8.302 % from Dec 2019 (Median) to 2023, with 5 observations. The data reached an all-time high of 9.741 % in 2022 and a record low of 7.654 % in 2020. Botswana Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: % Cumulative: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Botswana – Table BW.World Bank.WDI: Social: Education Statistics. The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;;

Botswana: Female literacy rate, ages 15-24: Pour cet indicateur, UNESCO fournit des données pour la Botswana de 1991 à 2013. La valeur moyenne pour Botswana pendant cette période était de 95.71 pour cent avec un minimum de 92 pour cent en 1991 et un maximum de 99.14 pour cent en 2013.

Abstract

The Southern Africa Consortium for Monitoring Educational Quality (SACMEQ) is a consortium of Ministries of Education and Culture located in the Southern Africa subregion. This consortium works in close partnership with the International Institute for Educational Planning (IIEP). SACMEQ’s main aim is to undertake co-operative educational policy research in order to generate information that can be used by decision-makers to plan the quality of education. SACMEQ’s programme of educational policy research has four features which have optimized its contributions to the field of educational planning: (1) it provides research-based policy advice concerning high-priority educational quality issues that have been identified by key decision-makers in Southern Africa, (2) it functions as a co-operative venture based on a strong network of Ministries of Education and Culture, (3) it combines research and training components that are linked with institutional capacity building, and its future directions are defined by participating ministries. In each participating country, a National Research Co-ordinator is responsible for implementing SACMEQ’s projects.

The SACMEQ I Project commenced in 1995 and was completed in 1999. The SACMEQ I main data collection was implemented in seven SACMEQ Ministries of Education (Kenya, Mauritius, Malawi, Namibia, Zambia, Zanzibar, and Zimbabwe). The study provided "agendas for government action" concerning: educational inputs to schools, benchmark standards for educational provision, equity in the allocation of educational resources, and the reading literacy performance of Grade 6 learners. The data collection for this project included information gathered from around 20,000 learners; 3,000 teachers; and 1,000 school principals.

This co-operative sub-regional educational research project collected data in order to guide decisionmaking in these countries with respect to questions around high priority policy issues. These included: • What are the baseline data for selected inputs to primary schools? • How do the conditions of primary schooling compare with the Ministry of Education and Culture’s own bench-mark standards? • Have educational inputs to schools been allocated in an equitable fashion? • What is the basic literacy level among pupils in upper primary school? • Which educational inputs to primary schools have most impact on pupil reading achievement at the upper primary level?

In 1995 there were five fully active members of SACMEQ: Mauritius, Namibia, Zambia, Tanzania (Zanzibar), and Zimbabwe. These Ministries of Education and Culture participated in all phases of SACMEQ’s establishment and its initial educational policy research project. There are also four partially active members of SACMEQ: Kenya, Tanzania (Mainland), Malawi, and Swaziland. These Ministries of Education and Culture have made contributions to the preparation of the Project Plan for SACMEQ’s initial educational policy research project. Three other countries (Botswana, Lesotho, and South Africa) had observer status due to their involvement in SACMEQ related training workshops or their participation in some elements of the preparation of the first proposal for launching SACMEQ.

Geographic coverage

National Coverage

Analysis unit

• Pupils
• Schools
• Teachers

Universe

The target population for SACMEQ's Initial Project was defined as "all pupils at the Grade 6 level in 1995 who were attending registered government or non-government schools". Grade 6 was chosen because it was the grade level where the basics of reading literacy were expected to have been acquired.

Kind of data

Sample survey data [ssd]

Sampling procedure

A stratified two-stage sample design was used to select around 150 schools in each country. Pupils were then selected within these schools by drawing simple random samples. A more detailed explanation of the sampling process is available under the 'Sampling' section of the report provided as external resources.

All sample designs applied in SACMEQ'S initial project were selected so as to meet the standards set down by the International Association for the Evaluation of Education Achievement (Ross, 1991). These standards require sample estimates of important pupil population characteristics to be (a) adjusted by weighing procedures designed to remove the potential for bias that may arise from different probabilities of selection, and (b) have sampling errors for the main criterion variables that are of the same magnitude or smaller than a simple random sample of 400 pupils (thereby providing 95 percent confidence limits for sample estimates of population percentages of plus or minus 5 percentage points, and 95 percent confidence limits for sample estimates of population means of plus or minus one tenth of a pupil standard deviation unit).

The desired target population in Zambia was 'all pupils at the Grade 6 level in the eleventh month of the school year, 1995, who were attending registered government and grant-aided schools in the country'. The number of schools and pupils in the desired, excluded, and defined population have been presented in Table 2.2 of the Sample Report provided as external resources. From the defined target population a probability sample of schools (with probability proportional to the Grade 6 enrolment in each school) was drawn. This resulted in a planned national sample of 165 schools and 3,300 pupils. This sample design was designed to yield an equivalent sample size' (Ross and Wilson, 1994) of 400 pupils - based on an estimated intra-class correlation (rho) for pupil reading test scores of around 0.30. In fact, after the rho was calculated for the reading scores, it was found to be 0.3 1 - which was about the same as had been expected At the first stage of sampling, schools were selected with a probability proportional to the number of pupils who were members of the defined target population. To achieve this selection a 'random start - constant interval' procedure was applied (Ross, 1987). In several strata there were some schools with numbers of pupils in the defined target population that exceeded the size of the 'constant interval', and therefore each of these schools was randomly broken into smaller 'pseudo schools' before the commencement of the sampling. At the second stage of sampling, a simple random sample of 20 pupils was selected within each selected school. Sampling weights were used to adjust for the disproportionate allocation of the sample across districts and also to account for the small loss of student data due to absenteeism on the day of the data collection.

Mode of data collection

Face-to-face [f2f]

Research instrument

The data collection for SACMEQ's Initial Project took place in October 1995 and involved the administration of questionnaires to pupils, teachers, and school heads. The pupil questionnaire contained questions about the pupils' home backgrounds and their school life; the teacher questionnaire asked about classrooms, teaching practices, working conditions, and teacher housing; and the school head questionnaire collected information about teachers, enrolments, buildings, facilities, and management. A reading literacy test was also given to the pupils. The test was based on items that were selected after a trial-testing programme had been completed.

The SACMEQ Data Collection Instruments include the following documents: - SACMEQ Questionnaires - which are administered to pupils, teachers, and school heads. - SACMEQ Tests - which are administered to pupils and teachers (covering reading mathematics, and HIV-AIDS knowledge). - Other SACMEQ Data Collection Instruments - such as take-home pupil questionnaires, school context proformas, and within-school project management documents.

Cleaning operations

All of the team leaders for the data collectors returned the instruments to the Ministry Headquarters (for the attention of the NRC), during the second week after the test administration. Once the instruments were returned to the Headquarters, three data entry staff within the Statistical Section of the Ministry entered the data, using the Data Entry Manager (DEM) a software programme developed at the IIEP (Schleicher, 1995). This software was adapted specifically for the entry of SACMEQ data. The data entry took six weeks and the data were sent on diskette to IIEP in March, 1996. It must be mentioned that at the time of data entry, the earlier version of the DEM structure files was used, and this caused major problems in cleaning the data at a later stage and reconstituting the structure of the files as they were meant to be.

Response rate

The planned sample was designed to contain 165 schools allocated across provinces, as shown in the first column of figures in Table 2.3 of the Survey Report provided as external resources. The achieved sample of schools was 157. The response rates for the sample have been recorded in Table 2.3. The percentage response for schools was 95.2 percent and that of pupils was 77.5 percent. The non-responding pupils were those who were absent on the day of testing. By province, this absenteeism varied from 2 to 12 percent.

Sampling error estimates

In the survey report provided as external resources, standard errors were provided for all important variables. The calculation of these errors acknowledged that the sample was not a simple random sample - but rather a complex two-stage cluster sample that included weighting adjustments to compensate for variations in selection probabilities. The errors were

Abstract

The ultimate objective of the BMTHS is to have a high frequency programme of household surveys that is predictable, flexible and amenable to the ever increasing and changing data needs for government, private sector, planners and researchers. The BMTHS provides a permanent platform for the collection of socio-economic data. This is in contrast to the inter-censal programme of surveys, which is, to a large extent adhoc in nature in that the surveys are infrequent, and the emerging stakeholder needs which have not been planned for are done on adhoc basis.

Specific Survey objectives

It is imperative that a well-coordinated, predictable data provision framework is put in place in the form of BMTHS, as compared to the inter-censal programme of surveys. The BMTHS will provide more frequent, stakeholder specific data that enable policy makers and planners to use real time data in formulation of policies and programmes. The continuous (yearly) nature of the BMTHS allows for close monitoring of programmes, ensuring timely interventions and programme/policy fine tuning. This will lead to robust, responsive relevant programmes that would ultimately improve on the livelihoods of Batswana and the economy. Savings on development budget would be realized due to effective informed policies and programmes.

The BMTHS is set out to;

· provide socio-demographics of the Botswana population; · Provide poverty datum line (PDL) in the country · Provide a list of indicators that monitor poverty
· Provide disaggregated information on poverty levels for monitoring and evaluation of eradication programmes on more regular basis · Continuously provide profiles of poor households. · Profile the poor to assist stakeholders identify the poor among the population · Provide household expenditure information to be used in re-basing of Consumer Price Index · Measures of both current and usual economic activity · Obtain a measure of the size of employment in both formal and informal sectors · Measure of unemployment and underemployment · Determine the size of economically active and inactive population · Provide information on education attainment, occupation and employment status · Determine the impact of education and health among on poor population; · Determine the impact of agriculture among poor population.

Survey Methodology

The Survey methodology outlines the sampling, data collection, processing, publicity and analysis methodologies and strategies employed in the conduct of the BMTHS.

Survey Sampling The Botswana Multi-Topic Household Survey like most national surveys, employed a two stage stratified sampling design. The procedure was made plausible by the existing stratification of twentyseven (27) census districts which are heterogeneous in nature and are aligned to administrative districts. In this structure, the census districts were further grouped into three (3) domains, being; cities/ towns, urban villages and rural areas. The survey only targeted households in all districts and sub-districts. It did not cover institutions such as prisons, army barracks, hospitals and other institutions because the survey was meant to investigate poverty and employment levels at households and individual level.

Geographic coverage

The coverage- nation-wide using administrative district and sub-districts that are usually used by Statistics Botswana in most surveys and censuses

Analysis unit

individuals, households, and communities.

Universe

The survey only targeted households in all districts and sub-districts. It did not cover institutions such as prisons, army barracks, hospitals and other institutions because the survey was meant to investigate poverty and employment levels at households and individual level.

Sampling procedure

The Botswana Multi-Topic Household Survey like most national surveys, employed a two stage stratified sampling design. The procedure was made plausible by the existing stratification of twenty seven (27) census districts which are heterogeneous in nature and are aligned to administrative districts. In this structure, the census districts were further grouped into three (3) domains, being; cities/ towns, urban villages and rural areas. The survey only targeted households in all districts and sub-districts. It did not cover institutions such as prisons, army barracks, hospitals and other institutions because the survey was meant to investigate poverty and employment levels at households and individual level. Botswana Multi-Topic Household Survey Report 2015/16 Statistics Botswana In light of the above, the first stage was the selection of Enumeration Areas (EAs) as Primary Sampling Units (PSUs) with Probability Proportional to Size (PPS) where measure of size is the number of households in an EA as defined in the 2011 Population & Housing Census. This yielded 599 Enumeration Areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

A household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphan hood status.

Cleaning operations

The data editing should contain information on how the data was treated or controlled for in terms of consistency and coherence. This item does not concern the data entry phase but only the editing of data whether manual or automatic. - Was a hot deck or a cold deck technique used to edit the data? - Were corrections made automatically (by program), or by visual control of the questionnaire? - What software was used?

If materials are available (specifications for data editing, report on data editing, programs used for data editing), they should be listed here and provided as external resources.

Example:

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of SPSS data files Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource.

Response rate

Response rates for the survey

Variable Estimates Response Response rate
Enumeration Areas (PSU) 599 598 99.8
Households (SSU) 7,188 7,060 98.2
Persons Participation 25,130 24,720 98.4

Sampling error estimates

For sampling surveys, it is good practice to calculate and publish sampling error. This field is used to provide information on these calculations. This includes: - A list of ratios/indicators for which sampling errors were computed. - Details regarding the software used for computing the sampling error, and reference to the programs used (to be provided as external resources) as the program used to perform the calculations. - Reference to the reports or other document where the results can be found (to be provided as external resources).

Example:

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the 2005-2006 MICS to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.